Search Results (16,415)

Surface congestion and taxiing delays at high-density airports increasingly constrain aviation sustainability, as ground-phase fuel consumption and emissions constitute a significant share of total airport emissions. Existing studies typically decouple air traffic flow management from ground resource scheduling, hindering coordinated optimization of punctuality, environmental benefits, and resource utilization. This paper proposes a multi-objective optimization method for runway-taxiway systems oriented toward air–ground collaborative decision-making, integrating Calculated Take-Off Time (CTOT) compliance constraints. A tri-objective mixed-integer programming model is formulated to minimize CTOT deviation, total taxiing time, and runway workload imbalance. A hybrid intelligent algorithm, SSA-SCA-NSGA-II, is designed with a bidirectional elite feedback mechanism to address this NP-hard problem. Validation uses real operational data of 58 departure flights during a peak period at Beijing Daxing International Airport. The results demonstrate that the proposed method achieves effective trade-offs on the Pareto front: CTOT compliance rate increased from 77.6% to 89.7–96.6%; total taxiing time decreased from 692 min to 551–635 min; and dual-runway utilization imbalance declined from 5.2% to 1.7–3.8%. These improvements translate into quantifiable sustainability gains: fuel consumption is reduced by 1425–3525 kg and CO₂ emissions by 4503–11,139 kg per peak hour, alongside a 19-percentage point improvement in punctuality that lowers passenger delay costs and reduces controller coordination workload. By simultaneously advancing environmental sustainability (carbon footprint reduction), economic sustainability (fuel and operational cost savings), and social sustainability (service punctuality and labor efficiency), the framework provides a measurable, monitorable, and policy-relevant decision-support tool for green airport surface operations aligned with sustainable development goals (SDGs). Full article

We re-conceptualize corporate debt default risk (EDF) as an emergent state variable of a coupled financial system and ask how capital-market opening reshapes its equilibrium. Extending the structural credit-risk framework with three interacting subsystem channels—external financing, investment efficiency, and information disclosure—we derive a closed-form result showing that an exogenous increase in liberalization strictly reduces the system-level corporate debt default probability through three complementary channels. We then exploit the staggered roll-out of China’s Shanghai–Hong Kong and Shenzhen–Hong Kong Stock Connect (HSGT) programs as a quasi-natural experiment on a panel of 21,351 firm-year observations over 2011–2023. A difference-in-differences (DID) estimator confirms a significant stabilizing effect on the firm’s market-implied default probability that is robust to an extensive battery of identification and specification checks; mechanism regressions confirm all three model-implied channels. The stabilizing effect is further amplified in firms facing greater environmental uncertainty and greater customer concentration—precisely the regimes in which our model predicts the underlying subsystem coupling to be most fragile. Our findings recast capital-market opening as a system-level intervention that simultaneously re-balances financing, investment, and information subsystems of the financial system, with implications for financial-stability policy in emerging economies. Full article

The paper examines the macroeconomic and environmental outcomes associated with green financing across 22 emerging markets and developing economies from 2002 to 2024. Driven by the increased policy focus on climate finance as a two-fold tool of sustainability and development, the analysis assesses whether green financing is an economic growth driver. A multi-model structure is used (fixed effects, non-linear (quadratic), threshold, dynamic (lagged), and first-difference specifications) to achieve strength and eliminate model-specific bias. The findings show that green financing exhibits a weak positive association with economic growth in baseline and regime specifications. Still, this relationship is not robust across dynamic and first-difference models. Moreover, there is no indication of non-linearity or a threshold effect (a Green Laffer Curve). Patterns that indicate a weak positive relationship are cross-sectional and not robust to panel estimation; they are therefore aggregation-biased. Conversely, green financing has a low negative correlation with CO₂ emissions, indicating partial environmental efficiency. The results show that climate finance is limited in scale and inefficiently structured, which limits its macroeconomic impact. In general, the paper concludes that green finance, although environmentally applicable, is not sufficient as it currently stands to spur economic growth in emerging economies. Full article

For advancing carbon peaking and neutrality objectives and regional socio-ecological sustainability, it is critical to examine how land use change and ecosystem carbon storage may evolve under different development scenarios, and to reveal the spatiotemporal patterns and key drivers of carbon sink capacity across the Lower Yangtze River Basin. Such analysis bears both substantial scientific insight and practical relevance. By coupling the PLUS, InVEST, and Geographical Detector models, the present study conducted a comprehensive assessment of land use and carbon storage dynamics in the Lower Yangtze River region from 2000 to 2025. We further explored how different factors drive the spatiotemporal variation in carbon storage, and predicted the potential land use patterns and associated carbon storage values in the research area by 2030 under three hypothetical scenarios. Collectively, our analysis yielded four core conclusions. (1) Between 2000 and 2025, the land use transformation in the research area was dominated by the continuous shrinkage of arable land and the expansion of construction land. (2) The total carbon storage in the study area declined steadily throughout the study period, showing distinct phased characteristics with a steep drop in the early stage and a slower decline thereafter. (3) Implementing the S2 scenario could effectively curb regional carbon storage loss, whereas the S3 Scenario would result in the most severe carbon stock depletion. (4) The spatial configuration of carbon storage is primarily structured by natural environmental factors. In light of these research outcomes, several recommendations are proposed to guide regional sustainable development. Specifically, efforts should be made to improve the intensive use of urban construction land, thereby minimizing carbon storage loss caused by urbanization. Additionally, develop scientific and targeted ecological conservation policies based on the spatial distribution patterns of high carbon storage zones. Finally, implementing regionally tailored management measures will help achieve coordinated and sustainable development across the study area. Full article

A sustainable low-carbon transition requires policy that curbs emissions while accelerating green technological innovation. The EU Carbon Border Adjustment Mechanism (CBAM) imposes carbon costs on high-emission exports; yet, how it shapes exporters’ green innovation remains poorly understood. We develop an open-economy Environmental Dynamic Stochastic General Equilibrium (E-DSGE) model embedding three CBAM transmission channels: cost-driven (higher carbon-intensive production costs), awareness-driven (firms’ forward-looking expectations), and information-enhancement (lower green R&D financing costs). The model decomposes CBAM’s green-innovation effects by jointly endogenizing forward-looking green R&D investment and carbon disclosure quality in general equilibrium. Calibrated to Chinese data and solved in Dynare 7.0, the model is simulated over forty quarters. Under the baseline calibration, simulations suggest a CBAM shock raises green R&D investment by approximately 6.5% at its peak and the green technology level by approximately 12.5% by quarter 40, while brown emission intensity falls by approximately 10%. Within this window the policy carries a net welfare cost of approximately 0.34% of steady-state consumption, concentrated in transition-period labor disutility, with most gains accruing later. Combining CBAM with R&D subsidies modestly reduces the within-window welfare cost and raises long-run green technology. Realizing this sustainability potential requires policy credibility, carbon-information infrastructure, and coordinated innovation support. Full article

This study examines the role of microfinance institutions (MFIs) in promoting environmental sustainability in Zimbabwe’s artisanal gold mining (AGM) sector, a context characterised by significant ecological degradation and weak regulatory enforcement. While previous studies have focused on MFIs’ role in financial inclusion, their potential to promote environmental sustainability in the informal AGM sector remains underexplored in sub-Saharan Africa. Using an interpretivist qualitative case study design, the study draws on in-depth interviews with MFI personnel operating in AGM regions. The findings of this study suggest that MFIs have the potential to influence environmental sustainability through mechanisms such as conditional lending, financial incentives, and environmental awareness programs within AGM sectors in which they operate. The effectiveness of MFIs in advancing environmental sustainability, however, is constrained by weak regulatory frameworks, limited resources, and the sector’s informal nature. The study contributes to the emerging literature on green microfinance by providing context-specific insights into how financial institutions function as intermediaries of environmental governance. Practically, the findings highlight the need for policy frameworks that integrate environmental considerations into microfinance operations and strengthen institutional coordination among stakeholders. Full article

This study empirically analyses the influence of energy consumption, economic growth, and foreign direct investment (FDI) on carbon emission in India. The Autoregressive Distributed Lag (ARDL) bounds-testing approach is applied on time series data from 1990 to 2022 to determine the cointegration between series variables. The findings show that all variables are cointegrated. The Granger Causality test confirms unidirectional causality running from economic growth to carbon emissions, from carbon emissions to energy consumption, from energy consumption to foreign direct investment, and from foreign direct investment to renewable energy consumption. Also, the results presented a bidirectional causal relationship between foreign direct investment and carbon emission. Thus, the level of carbon emissions is significantly connected with economic growth and energy consumption. The rising energy demand further supports investment in the energy sector. Based on our findings, this study suggests the creation of policies towards mitigation of environmental pollution and promotion of investment in clean energy sources. Full article

Extreme meteorological and climate events, such as floods and prolonged droughts, cause socio-environmental disasters in several regions of the world, including the Amazon. The Amazon Delta-Estuary, which is shaped by coastal and river environments, has constantly changing land use patterns, making it vulnerable to such events. This study aimed to evaluate rainfall regimes and flood scenarios in the tributaries of the Amazon Delta-Estuary, known as the Ottocoded River sub-basins, which are located in the municipalities of Abaetetuba and Barcarena in the Brazilian state of Pará. The Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset was used to analyze rainfall dynamics. The Height Above the Nearest Drainage (HAND) model was employed for flood modeling. The geomorphological, altimetric, bathymetric, and hydrodynamic components of the water bodies were also analyzed. Flood susceptibility was zoned as follows: Very High, High, Medium, Low and Very Low. Of the surveyed buildings, 28.8% were classified as ‘Very High’, 29.1% as ‘High’, 20.2% as ‘Medium’, 13.4% as ‘Low’, and 8.5% as ‘Very Low’. Many of these buildings are located in flood-prone areas, posing a significant risk of socio-environmental disasters. This research could inform public policies aimed at managing the risks associated with environmental disasters and extreme events. Full article

The transition to a circular economy (CE) in the automotive sector is increasingly critical amid rising resource pressures and climate imperatives. In India, this shift is influenced by regulatory initiatives, corporate sustainability goals, and life-cycle-wide environmental challenges. However, current studies remain fragmented, often neglecting the linkages between policy drivers, material issues, and firm-level responses. This study aims to evaluate how CE strategies are operationalized across the Indian automotive value chain using a Drivers–Materiality–Response (DMR) analytical framework. A multiple-case qualitative analysis was conducted involving six major automotive firms and associated ecosystem actors, with data sourced from corporate reports, national policies, and third-party assessments from 2018 to 2024. Semi-structured interviews with 11 industry experts were incorporated to strengthen triangulation, validate firm-level circular economy claims, and support the reliability of the DMR-based interpretation. Findings reveal strong alignment with national CE policies among leading firms, particularly Tata Motors and Mahindra, with comprehensive integration of electrification, battery reuse, zero-waste goals, and digital mobility solutions. However, challenges remain in end-of-life vehicle (ELV) formalization and circularity in downstream systems. The DMR model effectively bridges gaps in existing frameworks by offering a life-cycle-based lens that links Environmental, Social and Governance (ESG), Life Cycle Assessment (LCA), and policy–firm dynamics. The study contributes a scalable diagnostic tool for assessing CE maturity in emerging economies. While limited by reliance on secondary data, the triangulated approach enhances reliability and provides actionable insights for policymakers and industry leaders. Full article

Urban ecological resilience (UER) has become a central concern in rapidly urbanizing regions where development pressures increasingly interact with ecological constraints. Focusing on the Guanzhong Plain Urban Agglomeration (GPUA), a semi-arid urban agglomeration in western China, this study examines the non-static and locally heterogeneous drivers of UER across 11 prefecture-level cities from 2000 to 2023. UER is measured through resistance, adaptability, and recovery. An extended STIRPAT model, Elastic Net with stability selection, two-way fixed-effects period interactions, and Geographically and Temporally Weighted Regression (GTWR) are integrated to identify robust drivers, test post-2011 shifts, and estimate city-year local associations. Residual Moran’s I diagnostics and Spatial Lag GTWR (SLM-GTWR) are used as supplementary checks. The results show that UER remains relatively stable at the aggregate regional level but becomes increasingly divergent across cities. Ten robust drivers are retained, with fiscal investment intensity, human capital, medical and health level, and total energy consumption emerging as key variables. Period heterogeneity results indicate that fiscal investment becomes more favorably associated with UER after 2011, while the marginal association of energy consumption weakens. GTWR reveals clear local heterogeneity: human capital shows the most stable positive association, medical and health level remains generally negative, fiscal investment is positive but context-dependent, and energy consumption is predominantly negative but locally differentiated. Supplementary spatial diagnostics suggest that the GTWR specification captures the main spatiotemporal structure of UER, while spatial-lag checks broadly support the robustness of the local coefficient patterns, although estimates of spatial interaction remain sensitive to how inter-city linkages are defined. These findings indicate that UER drivers are dynamic rather than fixed, with resilience formation shaped mainly by governance-regime shifts and localized heterogeneity. The study contributes a sequential screening–heterogeneity framework for identifying non-static resilience drivers and suggests that resilience governance should combine stage-sensitive policy adjustment, place-based intervention, and regional coordination where ecological functions and environmental risks cross administrative boundaries. Full article

Agri-food systems are increasingly exposed to environmental, economic, and social challenges, including climate change, biodiversity loss, resource depletion, and growing territorial inequalities. In this context, agroecology is increasingly recognised as a transformative paradigm integrating ecological, economic, social, cultural, and political dimensions within broader processes of food-system transition. Within the PRIMA AgrEcoMed project, 24 Italian agroecological initiatives led by women and young farmers were analysed to explore their contribution to agroecological transition processes in Mediterranean rural areas. The study adopts a qualitative multiple-case study approach and evaluates the selected initiatives through the framework of the 13 Principles of Agroecology proposed by the High-Level Panel of Experts on Food Security and Nutrition, organised into three operational axes: improving resource efficiency, strengthening resilience, and ensuring social responsibility and fairness. The results show that the analysed initiatives combine ecological farming practices with processes of multifunctionality, territorial networking, knowledge co-creation, short supply chains, and community engagement. The findings suggest that several initiatives move beyond input-reduction strategies associated with “weak agroecology” and display characteristics consistent with stronger agroecological pathways based on territorial embeddedness, collective learning, and the reorganisation of relationships between production, consumption, and local communities. The paper highlights the relevance of agroecology not only as an environmentally sustainable farming approach, but also as a broader socio-ecological and territorial transition process, as well as the importance of policy frameworks to support territorial agroecological systems. Full article

The Brazilian Cerrado, a global biodiversity hotspot, is under increasing pressure from agricultural expansion and native vegetation conversion, underscoring the need for efficient monitoring to support conservation and environmental policies. In heterogeneous landscapes, land use and land cover (LULC) mapping using supervised classification methods faces a major bottleneck: the need for extensive and high-quality training datasets. To address this challenge, we propose a semi-automated, clustering-based methodology for mapping secondary vegetation within previously deforested areas, reducing training-sample requirements and enabling scalable mapping through the clustering of satellite image time series. In the first stage, an unsupervised process integrates graphics processing unit (GPU)-accelerated Self-Organizing Maps and hierarchical clustering with Dynamic Time Warping to produce spectro-temporal clusters. In the second stage, specialists label and refine these clusters by visual interpretation, transferring expert knowledge from individual pixels to grouped spectro-temporal patterns. Applied to 692,000 km² of previously deforested land in the Cerrado biome, the methodology produced a mapped secondary vegetation area of 81,209 km² (11.74%). The design-based estimated area was 98,683 ± 10,071 km², with an overall accuracy of 96.45 ± 1.52%, a user’s accuracy of 96.27 ± 2.40%, a producer’s accuracy of 79.22 ± 7.94%, and an F1-score of 86.90%. The initial cluster labeling accounted for 86.3% of the final secondary vegetation area and limited the interpretation task to approximately 3000 cluster-level decisions. Implemented in the TerraClass Cerrado 2024 cycle, the workflow reduced the secondary vegetation mapping phase from approximately two years to six months while maintaining the thematic accuracy required for large-scale operational monitoring. Full article

Sustainable intensification of agriculture (SIA) aims to increase agricultural output while reducing or not increasing negative environmental impacts, yet evidence on the production effects of reduced chemical inputs remains limited. To narrow this gap, this study examines how SIA impacts grain production by treating China’s “Chemical Fertilizer Use Zero-Growth Policy” (Zero-Growth Policy) as a natural experiment, by using provincial-level data from 2008 to 2022. The study indicates the following: Firstly, the policy exerted no negative impact on grain production and even boosted grain production. Secondly, this production grain operated mainly through an increased grain sown-area share and higher cropping intensity, while yield per unit area remained statistically unchanged. Thirdly, heterogeneity analysis suggests that the positive association between the policy and grain production is relatively stronger in non-main grain production areas than in main grain production areas. These findings provide preliminary evidence that policy-oriented sustainable intensification may be compatible with grain output growth in the sample period. Full article

This study evaluates the performance of major types of conventional and bio-based plastic items commonly used in agriculture to provide comprehensive insights into their key structural and functional properties, including the chemical composition of the polymer matrix and additives, mechanical behavior, and thermal and radiometric properties. Twelve agricultural plastic (AP) items were analyzed: covering mulch films, geotextile ground cover, protection fleece and low tunnel fleece cover, fertilizer sack, fly trap, irrigation pipe, tree binding net, guide for tree, silage film and hay bales protection fabric. This selection of APs also encompasses a broad range of basic polymers, including conventional materials (mainly polyethylene and polypropylene) and bio-based formulations (primarily starch- or lignocellulose-containing blends). Mass spectrometry and infrared spectroscopy analyses were performed to assess polymer composition and additives. Mechanical properties were assessed through tensile and puncture tests; in addition, radiometric, thermogravimetric, surface wettability, water absorption and permeability tests were also performed to assess other relevant physical characteristics. The study identified significant differences among bio-based biodegradable APs and compared them with their conventional polyolefin-based counterparts. Material composition and structure were found to critically influence water interactions, shaping the balance between durability, degradation, and crop protection performance. Notably, bio-based mulch films exhibited higher water vapor permeability (0.6–1.1 × 10⁻¹³ g/m Pa s), reduced penetration resistance (12.1 N) and lowered impact and tensile strengths (21.8 MPa). Water interaction tests showed that the starch-based mulch film displayed very high swelling (above 100%), favoring biodegradation, whereas a biodegradable blend based on polyhydroxybutyrate and polybutylene succinate exhibited minimal swelling (<3%). Material composition and morphology were also key determinants of water vapor transport: dense polymer films provided superior moisture barriers (permeability range 0.013–0.04 × 10⁻¹³ g/m Pa s), while fibrous or biodegradable materials allowed enhanced vapor permeability. The results of this study, highlighting functionality, advantages and limitations of biodegradable APs versus conventional APs, are intended to guide future innovation in AP design, ensuring alignment with both the operational demands of modern agriculture and environmental sustainability goals. The data obtained from this study can support scientific advancements and policy recommendations on the use and management of plastics in agriculture. Full article

This systematic narrative review synthesizes 42 distinct sources including peer-reviewed journal articles, selected conference papers, and policy documents to examine the role of knowledge management (KM) processes in Saudi higher education accreditation, with specific focus on the National Center for Academic Accreditation and Evaluation (NCAAA) standards. Drawing on literature published between 2005 and 2025, the review investigates how KM frameworks, including Nonaka and Takeuchi’s SECI model (socialization, externalization, combination, and internalization), may be associated with accreditation outcomes in Saudi universities. The reviewed literature suggests an association between systematic KM approaches and more effective accreditation processes; causal conclusions are not warranted given the observational and case study nature of the evidence base. Certainty of the overall evidence body is rated as low to moderate. The study reveals significant challenges, including information decentralization, inadequate training, resistance to change, and the absence of dedicated governance structures that appear to impede effective knowledge transfer during accreditation processes. A secondary sustainability coding pass identified associations between KM-driven accreditation practices and institutional sustainability, environmental sustainability, and alignment with SDG 4 (Quality Education) and SDG 16 (Strong Institutions); these findings are hypothesis-generating rather than confirmatory. It should be noted that all screening and data extraction were conducted by a sole reviewer; a post hoc validation exercise achieved Cohen’s kappa = 0.81 (95% CI: 0.72–0.90) for inclusion/exclusion decisions, providing retrospective assurance of acceptable consistency. This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Full article